There are many kinds of mushrooms, especially poisonous mushrooms, which are similar in shape and difficult to identify. There is an important practical need for efficient identification of mushroom species. In view of the problems of complex background, low recognition accuracy, large number of model parameters and difficult deployment on mobile terminals in existing mushroom image recognition methods, a mushroom image recognition method based on improved ConvNeXt model and knowledge distillation is proposed. Firstly, the pre-trained ConvNeXt weight file is applied to the mushroom recognition task through transfer learning, and the coordinate attention mechanism is introduced to construct the ConvNeXt_CA model, which effectively improves the fine-grained feature extraction ability of the model. Secondly, based on the knowledge distillation technology, the ConvNeXt_CA model is used as the teacher model and the ShuffleNet v2 model is used as the student model to construct a lightweight MushNet model. The overall efficiency of the edge deployment of the improved model is greatly improved. Finally, the relevant model comparison experiments are carried out. The results show that the accuracy of the proposed improved model reaches 94.89%, and the size of the MushNet model after knowledge distillation is about 1/21 of the original, which is better than other traditional models and lightweight models. The effectiveness and feasibility of the proposed mushroom image recognition method are proved.